OpenRaiser/Intern-Atlas huggingface dataset:
LAMVI-2: A Visual Tool for Comparing and Tuning Word Embedding Models
Venue: arXiv cs.CL 2018
Year: 2018

Tuning machine learning models, particularly deep learning architectures, is a complex process. Automated hyperparameter tuning algorithms often depend on specific optimization metrics. However, in many situations, a developer trades one metric against another: accuracy versus overfitting, precision versus recall, smaller models and accuracy, etc. With deep learning, not only are the model's representations opaque, the model's behavior when parameters "knobs" are changed may also be unpredictable. Thus, picking the "best" model often requires time-consuming model comparison. In this work, we introduce LAMVI-2, a visual analytics system to support a developer in comparing hyperparameter settings and outcomes. By focusing on word-embedding models ("deep learning for text") we integrate views to compare both high-level statistics as well as internal model behaviors (e.g., comparing word 'distances'). We demonstrate how developers can work with LAMVI-2 to more quickly and accurately narrow down an appropriate and effective application-specific model.
Named Person Coreference in English News
Venue: arXiv cs.CL 2018
Year: 2018

People are often entities of interest in tasks such as search and information extraction. In these tasks, the goal is to find as much information as possible about people specified by their name. However in text, some of the references to people are by pronouns (she, his) or generic descriptions (the professor, the German chancellor). It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct person name. Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase. Our analysis reveals that standard coreference metrics do not reflect adequately the requirements in this task: they do not penalize systems for not identifying any mentions by name and they reward systems even if systems find correctly mentions to the same entity but fail to link these to a proper name (she--the student---no name). We introduce new metrics for evaluating named person coreference that address these discrepancies. We present a simple rule-based named entity recognition driven system, which outperforms the current state-of-the-art systems on these task-specific metrics and performs on par with them on traditional coreference evaluations. Finally, we present similar evaluation for coreference resolution of other named entities and show that the rule-based approach is effective only for person named coreference, not other named entity types.
Can Entropy Explain Successor Surprisal Effects in Reading?
Venue: arXiv cs.CL 2018
Year: 2018

Human reading behavior is sensitive to surprisal: more predictable words tend to be read faster. Unexpectedly, this applies not only to the surprisal of the word that is currently being read, but also to the surprisal of upcoming (successor) words that have not been fixated yet. This finding has been interpreted as evidence that readers can extract lexical information parafoveally. Calling this interpretation into question, Angele et al. (2015) showed that successor effects appear even in contexts in which those successor words are not yet visible. They hypothesized that successor surprisal predicts reading time because it approximates the reader's uncertainty about upcoming words. We test this hypothesis on a reading time corpus using an LSTM language model, and find that successor surprisal and entropy are independent predictors of reading time. This independence suggests that entropy alone is unlikely to be the full explanation for successor surprisal effects.
Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)
Venue: arXiv cs.DC 2018
Year: 2018

Experience shows that on today's high performance systems the utilization of different acceleration cards in conjunction with a high utilization of all other parts of the system is difficult. Future architectures, like exascale clusters, are expected to aggravate this issue as the number of cores are expected to increase and memory hierarchies are expected to become deeper. One big aspect for distributed applications is to guarantee high utilization of all available resources, including local or remote acceleration cards on a cluster while fully using all the available CPU resources and the integration of the GPU work into the overall programming model. For the integration of CUDA code we extended HPX, a general purpose C++ run time system for parallel and distributed applications of any scale, and enabled asynchronous data transfers from and to the GPU device and the asynchronous invocation of CUDA kernels on this data. Both operations are well integrated into the general programming model of HPX which allows to seamlessly overlap any GPU operation with work on the main cores. Any user defined CUDA kernel can be launched on any (local or remote) GPU device available to the distributed application. We present asynchronous implementations for the data transfers and kernel launches for CUDA code as part of a HPX asynchronous execution graph. Using this approach we can combine all remotely and locally available acceleration cards on a cluster to utilize its full performance capabilities. Overhead measurements show, that the integration of the asynchronous operations (data transfer + launches of the kernels) as part of the HPX execution graph imposes no additional computational overhead and significantly eases orchestrating coordinated and concurrent work on the main cores and the used GPU devices.
Automatic Identification and Ranking of Emergency Aids in Social Media Macro Community
Venue: arXiv cs.IR 2018
Year: 2018

Online social microblogging platforms including Twitter are increasingly used for aiding relief operations during disaster events. During most of the calamities that can be natural disasters or even armed attacks, non-governmental organizations look for critical information about resources to support effected people. Despite the recent advancement of natural language processing with deep neural networks, retrieval and ranking of short text becomes a challenging task because a lot of conversational and sympathy content merged with the critical information. In this paper, we address the problem of categorical information retrieval and ranking of most relevance information while considering the presence of short-text and multilingual languages that arise during such events. Our proposed model is based on the formation of embedding vector with the help of textual and statistical preprocessing, and finally, entire training 2,100,000 vectors were normalized using feed-forward neural network for need and availability tweets. Another important contribution of this paper lies in novel weighted Ranking Key algorithm based on top five general terms to rank the classified tweets in most relevance with classification. Lastly, we test our model on Nepal Earthquake dataset (contains short text and multilingual language tweets) and achieved 6.81% of mean average precision on 5,250,000 unlabeled embedding vectors of disaster relief tweets.
Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata.
A Miniaturized Semantic Segmentation Method for Remote Sensing Image
Venue: arXiv cs.CV 2018
Year: 2018

In order to save the memory, we propose a miniaturization method for neural network to reduce the parameter quantity existed in remote sensing (RS) image semantic segmentation model. The compact convolution optimization method is first used for standard U-Net to reduce the weights quantity. With the purpose of decreasing model performance loss caused by miniaturization and based on the characteristics of remote sensing image, fewer down-samplings and improved cascade atrous convolution are then used to improve the performance of the miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only achieves 29.26 times model compression, but also basically maintains the performance unchanged on the public dataset. We provide a Keras and Tensorflow hybrid programming implementation for our model: https://github.com/Isnot2bad/Micro-Net
Performance of Energy-Buffer Aided Incremental Relaying in Cooperative Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider a two-hop cooperative network with a direct link based on an energy harvesting (EH) decode-and-forward relay. The energy-buffer equipped relay harvests energy from the ambience, and uses the harvest-store-use (HSU) architecture. Since it is known that using a discrete-state Markov chain to model the energy buffer is inaccurate even for moderate number of states, we use a discrete-time continuous-state space Markov chain instead. We derive the limiting distribution of energy for both incremental on-off policy (IOFP) and the incremental best-effort policy (IBEF), and use them to obtain expressions for outage probability and throughput. The corresponding expressions for non-incremental signalling follow as a special case. We show that stable buffers using IBEP harness a diversity of two as compared to those using IOFP, which attain a diversity of one. However, while buffers using IBEF are consequently more reliable than those with IOFP, their throughput performance is only marginally superior. Simulation results are presented to validate the derived analytical expressions.
Agent-based models of collective intelligence
Venue: arXiv cs.MA 2018
Year: 2018

Collective or group intelligence is manifested in the fact that a team of cooperating agents can solve problems more efficiently than when those agents work in isolation. Although cooperation is, in general, a successful problem solving strategy, it is not clear whether it merely speeds up the time to find the solution, or whether it alters qualitatively the statistical signature of the search for the solution. Here we review and offer insights on two agent-based models of distributed cooperative problem-solving systems, whose task is to solve a cryptarithmetic puzzle. The first model is the imitative learning search in which the agents exchange information on the quality of their partial solutions to the puzzle and imitate the most successful agent in the group. This scenario predicts a very poor performance in the case imitation is too frequent or the group is too large, a phenomenon akin to Groupthink of social psychology. The second model is the blackboard organization in which agents read and post hints on a public blackboard. This brainstorming scenario performs the best when there is a stringent limit to the amount of information that is exhibited on the board. Both cooperative scenarios produce a substantial speed up of the time to solve the puzzle as compared with the situation where the agents work in isolation. The statistical signature of the search, however, is the same as that of the independent search.
Fabrik: An Online Collaborative Neural Network Editor
Venue: arXiv cs.LG 2018
Year: 2018

We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser. Fabrik provides a simple and intuitive GUI to import neural networks written in popular deep learning frameworks such as Caffe, Keras, and TensorFlow, and allows users to interact with, build, and edit models via simple drag and drop. Fabrik is designed to be framework agnostic and support high interoperability, and can be used to export models back to any supported framework. Finally, it provides powerful collaborative features to enable users to iterate over model design remotely and at scale.
Suspicious News Detection Using Micro Blog Text
Venue: arXiv cs.CL 2018
Year: 2018

We present a new task, suspicious news detection using micro blog text. This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles. Specifically, in this task, given a set of posts on SNS referring to a news article, the goal is to judge whether the article is to be verified or not. For this task, we create a publicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce the cost of manual fact-checking process.
Towards Smart City Innovation Under the Perspective of Software-Defined Networking, Artificial Intelligence and Big Data
Venue: arXiv cs.NI 2018
Year: 2018

Smart city projects address many of the current problems afflicting high populated areas and cities and, as such, are a target for government, institutions and private organizations that plan to explore its foreseen advantages. In technical terms, smart city projects present a complex set of requirements including a large number users with highly different and heterogeneous requirements. In this scenario, this paper proposes and analyses the impact and perspectives on adopting software-defined networking and artificial intelligence as innovative approaches for smart city project development and deployment. Big data is also considered as an inherent element of most smart city project that must be tackled. A framework layered view is proposed with a discussion about software-defined networking and machine learning impacts on innovation followed by a use case that demonstrates the potential benefits of cognitive learning for smart cities. It is argued that the complexity of smart city projects do require new innovative approaches that potentially result in more efficient and intelligent systems.
Computational geometry and the U.S. Supreme Court
Venue: arXiv cs.CG 2018
Year: 2018

We use the United States Supreme Court as an illuminative context in which to discuss three different spatial voting preference models: an instance of the widely used single-peaked preferences, and two models that are more novel in which vote outcomes have a strength in addition to a location. We introduce each model from a formal axiomatic perspective, briefly discuss practical motivation for each in terms of judicial behavior, prove mathematical relationships among the voting coalitions compatible with each model, and then study the two-dimensional setting by presenting computational tools for working with the models and by exploring these with judicial voting data from the Supreme Court.
Image Super-Resolution Using TV Priori Guided Convolutional Network
Venue: arXiv cs.MM 2018
Year: 2018

We proposed a TV priori information guided deep learning method for single image super-resolution(SR). The new alogorithm up-sample method based on TV priori, new learning method and neural networks architecture are embraced in our TV guided priori Convolutional Neural Network which diretcly learns an end to end mapping between the low level to high level images.
Multi-Spectral Imaging via Computed Tomography (MUSIC) - Comparing Unsupervised Spectral Segmentations for Material Differentiation
Venue: arXiv cs.CV 2018
Year: 2018

Multi-spectral computed tomography is an emerging technology for the non-destructive identification of object materials and the study of their physical properties. Applications of this technology can be found in various scientific and industrial contexts, such as luggage scanning at airports. Material distinction and its identification is challenging, even with spectral x-ray information, due to acquisition noise, tomographic reconstruction artefacts and scanning setup application constraints. We present MUSIC - and open access multi-spectral CT dataset in 2D and 3D - to promote further research in the area of material identification. We demonstrate the value of this dataset on the image analysis challenge of object segmentation purely based on the spectral response of its composing materials. In this context, we compare the segmentation accuracy of fast adaptive mean shift (FAMS) and unconstrained graph cuts on both datasets. We further discuss the impact of reconstruction artefacts and segmentation controls on the achievable results. Dataset, related software packages and further documentation are made available to the imaging community in an open-access manner to promote further data-driven research on the subject
Sequential anatomy localization in fetal echocardiography videos
Venue: arXiv cs.CV 2018
Year: 2018

Fetal heart motion is an important diagnostic indicator for structural detection and functional assessment of congenital heart disease. We propose an approach towards integrating deep convolutional and recurrent architectures that utilize localized spatial and temporal features of different anatomical substructures within a global spatiotemporal context for interpretation of fetal echocardiography videos. We formulate our task as a cardiac structure localization problem with convolutional architectures for aggregating global spatial context and detecting anatomical structures on spatial region proposals. This information is aggregated temporally by recurrent architectures to quantify the progressive motion patterns. We experimentally show that the resulting architecture combines anatomical landmark detection at the frame-level over multiple video sequences-with temporal progress of the associated anatomical motions to encode local spatiotemporal fetal heart dynamics and is validated on a real-world clinical dataset.
Complete Weight Distribution and MacWilliams Identities for Asymmetric Quantum Codes
Venue: arXiv cs.IT 2018
Year: 2018

In 1997, Shor and Laflamme defined the weight enumerators for quantum error-correcting codes and derived a MacWilliams identity. We extend their work by introducing our double weight enumerators and complete weight enumerators. The MacWilliams identities for these enumerators can be obtained similarly. With the help of MacWilliams identities, we obtain various bounds for asymmetric quantum codes.
Exploring Security Economics in IoT Standardization Efforts
Venue: arXiv cs.CR 2018
Year: 2018

The Internet of Things (IoT) propagates the paradigm of interconnecting billions of heterogeneous devices by various manufacturers. To enable IoT applications, the communication between IoT devices follows specifications defined by standard developing organizations. In this paper, we present a case study that investigates disclosed insecurities of the popular IoT standard ZigBee, and derive general lessons about security economics in IoT standardization efforts. We discuss the motivation of IoT standardization efforts that are primarily driven from an economic perspective, in which large investments in security are not considered necessary since the consumers do not reward them. Success at the market is achieved by being quick-to-market, providing functional features and offering easy integration for complementors. Nevertheless, manufacturers should not only consider economic reasons but also see their responsibility to protect humans and technological infrastructures from being threatened by insecure IoT products. In this context, we propose a number of recommendations to strengthen the security design in future IoT standardization efforts, ranging from the definition of a precise security model to the enforcement of an update policy.
On Coding for Reliable VNF Chaining in DCNs
Venue: arXiv cs.PF 2018
Year: 2018

We study how erasure coding can improve service reliability in Data Center Networks (DCN). To this end, we find that coding can be best deployed in systems, where i) traffic is split into multiple parallel sub-flows, ii) each sub-flow is encoded; iii) SFC along with their corresponding Virtual Network Functions (VNF) concatenated are replicated into at least as many VNF instances as there are sub-flows, resulting in parallel sub- SFCs; and iv) all coded sub-flows are distributed over parallel paths and processed in parallel. We study service reliability as function of the level of parallelization within DCN and the resulting amount of redundancy. Based on the probability theory and by considering failures of path segments, VNF and server failures, we analytically derive the probability that parallel subflows are successfully processed by the parallelized SFC and that the original serial traffic can be successfully recovered without service interruptions.We compare the proposed failure protection with coding and the standard backup protection and evaluate the related overhead of both methods, including decoding, traffic redirection and VNF migration. The results not only show the benefit of our scheme for reliability, but also a reduced overhead required in comparison to backup protection.
Implementing Linking in Multiparty Sessions (Extended Abstract)
Venue: arXiv cs.PL 2018
Year: 2018

The fast growth of service-oriented programming (SOP) is evident in this day and age of the Internet, and handling communication is of paramount importance in SOP. Session types are a formalism that is proposed to specify interactions between communicating processes. In essence, a session type system is a kind of type system designed to enforce (through type-checking) that the involved processes communicate according to a chosen protocol specified as a session type. It is well-known that linear logic plays a pivotal role in the study of session types. For instance, various inference rules in linear logic can be interpreted as ways for constructing channels (used by communicating processes to send/receive messages.) A particularly interesting case is the cut-rule in linear logic, which can be interpreted as a way for connecting the ends of two matching channels to form a single new channel. This form of channel construction is often referred to as linking or (bi-directional) forwarding. We have generalized classical linear logic into classical linear multirole logic (LMRL), where the former can be seen as a special case of the latter involving only two roles. In LMRL, there is a cut-rule involving multiple sequents (instead of exactly two), which we call multiparty cut (mp-cut). We have also formulated a novel multiparty session type system directly based on LMRL. When implementing it, we need to find a way of connecting multiple channels that corresponds to mp-cut. In this paper, we describe an implementation of linking for multiparty sessions in the setting of shared memory. We also describe two novel concepts, two-way linking with residual and three-way linking, which can only be formulated in the setting of multiparty sessions. Notably, linking for binary sessions can be thought of as a specially optimized version of what is implemented for multiparty sessions.